Spatial regularization applied to factor analysis of medical image sequences (FAMIS).

Dynamic image sequences allow physiological mechanisms to be monitored after the injection of a tracer. Factor analysis of medical image sequences (FAMIS) hence creates a synthesis of the information in one image sequence. It estimates a limited number of structures (factor images) assuming that the tracer kinetics (factors) are similar at each point inside the structure. A spatial regularization method for computing factor images (REG-FAMIS) is proposed to remove irregularities due to noise in the original data while preserving discontinuities between structures. REG-FAMIS has been applied to two sets of simulations: (a) dynamic data with Gaussian noise and (b) dynamic studies in emission tomography (PET or SPECT), which respect real tomographic acquisition parameters and noise characteristics. Optimal regularization parameters are estimated in order to minimize the distance between reference images and regularized factor images. Compared with conventional factor images, the root mean square error between regularized images and reference factor images is improved by 3 for the first set of simulations, and by about 1.5 for the second set of simulations. In all cases, regularized factor images are qualitatively and quantitatively improved.